Analysis and Reporting Guidelines for Bayesian Graphical Modeling of Cross-Sectional Data: A Tutorial in JASP
Abstract
Bayesian graphical modeling (BGM) is an emerging approach to psychological network analysis that offers proper uncertainty quantification and allows for incorporating prior information. However, resources that integrate the full analytic workflow within user-friendly software and provide guidance for transparent reporting remain scarce. In this tutorial, we address this gap by offering an integrated analysis and reporting guide for BGM. We first briefly outline the theoretical foundations of the framework. We then introduce the updated Bayesian network analysis module in the open-source software JASP and demonstrate three core analyses using an empirical dataset: edge selection and parameter estimation, clustering analysis, and comparing networks across groups. For each analysis, we provide minimal template paragraphs for the Methods and Results sections. We conclude with recommendations for prior robustness analyses and open research practices.
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BibTeX
@misc{sekulovski2026analysis,
title = {{Analysis and Reporting Guidelines for Bayesian Graphical Modeling of Cross-Sectional Data: A Tutorial in JASP}},
author = {Nikola Sekulovski and Karoline Huth and Don van den Bergh and Johnny van Doorn and Tessa F. Blanken and Eric-Jan Wagenmakers and Maarten Marsman},
howpublished = {PsyArXiv},
year = {2026},
month = oct,
doi = {10.31234/osf.io/xce7j_v1},
url = {https://osf.io/preprints/psyarxiv/xce7j_v1/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Analysis and Reporting Guidelines for Bayesian Graphical Modeling of Cross-Sectional Data: A Tutorial in JASP
AU - Nikola Sekulovski
AU - Karoline Huth
AU - Don van den Bergh
AU - Johnny van Doorn
AU - Tessa F. Blanken
AU - Eric-Jan Wagenmakers
AU - Maarten Marsman
PY - 2026
DA - 2026/10/20
PB - PsyArXiv
DO - 10.31234/osf.io/xce7j_v1
UR - https://osf.io/preprints/psyarxiv/xce7j_v1/
AB - Bayesian graphical modeling (BGM) is an emerging approach to psychological network analysis that offers proper uncertainty quantification and allows for incorporating prior information. However, resources that integrate the full analytic workflow within user-friendly software and provide guidance for transparent reporting remain scarce. In this tutorial, we address this gap by offering an integrated analysis and reporting guide for BGM. We first briefly outline the theoretical foundations of the framework. We then introduce the updated Bayesian network analysis module in the open-source software JASP and demonstrate three core analyses using an empirical dataset: edge selection and parameter estimation, clustering analysis, and comparing networks across groups. For each analysis, we provide minimal template paragraphs for the Methods and Results sections. We conclude with recommendations for prior robustness analyses and open research practices.
ER -